[1]} proposed graph convolutional networks (GCN) which work on graph structures. Here, we describe the GCN which is used in our model. We represent a graph \(\mathcal {G}\)  with \(m\)  nodes using an adjacency matrix \(\mathbf {A}\)  of size \(m \times m\) . If there is an edge between node \(i\)  and node \(j\) , then \(A_{ij} = A_{ji} = 1\) . Self loops, \(A_{ii} = 1\) , are added in the graph \(\mathcal {G}\)  so that the nodes keep their own information too. We normalize the adjacency matrix \(\mathbf {A}\)  by using symmetric normalization proposed by [1]}. A diagonal node degree matrix \(\mathbf {D}\)  of size \(m \times m\)  is used in the normalization of \(\mathbf {A}\) .
\(&D_{ij}={\left\lbrace \begin{array}{ll}\text{deg}(v_i) & \text{if } i=j\\0 & \text{otherwise}\end{array}\right.}\nonumber \\&D^{-\frac{1}{2}}_{ij} = {\left\lbrace \begin{array}{ll}\frac{1}{\sqrt{D}_{ij}} & \text{if } i=j\\0 & \text{otherwise}\end{array}\right.}\nonumber \\&\hat{\mathbf {A}} = \mathbf {D}^{-\frac{1}{2}} \mathbf {A} \mathbf {D}^{-\frac{1}{2}}\) 
